This research paper focuses on the problem of dynamic objects and their impact on effective motion planning and localization. The paper proposes a two-step process to address this challenge, which involves finding the dynamic objects in the scene using a Flow-based method and then using a deep Video inpainting algorithm to remove them. The study aims to test the validity of this approach by comparing it with baseline results using two state-of-the-art SLAM algorithms, ORB-SLAM2 and LSD, and understanding the impact of dynamic objects and the corresponding trade-offs. The proposed approach does not require any significant modifications to the baseline SLAM algorithms, and therefore, the computational effort required remains unchanged. The paper presents a detailed analysis of the results obtained and concludes that the proposed method is effective in removing dynamic objects from the scene, leading to improved SLAM performance.

翻译：本研究论文专注于动态物体问题及其对于有效运动规划和定位的影响。本文提出了一种二步法来解决这个挑战，包括使用基于流的方法找到场景中的动态物体，然后使用深度视频修复算法进行去除。本研究旨在通过比较使用两种最先进的SLAM算法（ORB-SLAM2和LSD）的基线结果，测试该方法的有效性，并了解动态物体及其相关权衡的影响。所提出的方法不需要对基线SLAM算法进行任何重大修改，因此所需的计算工作量保持不变。本文提供了详细的结果分析，并得出结论，认为所提出的方法在从场景中去除动态物体方面是有效的，从而得到了改善的SLAM性能。